Update README.md

README.md

@@ -1,61 +1,57 @@
 ---
-{}
+license: apache-2.0
+language:
+- en
+tags:
+- hearing loss
+- challenge
+- signal processing
+- source separation
+- audio
+- audio-to-audio
+- Causal
 ---
 
-        ---
-        license: apache-2.0
-        language:
-        - en
-        tags:
-        - hearing loss
-        - challenge
-        - signal processing
-        - source separation
-        - audio
-        - audio-to-audio
-        - Causal
-        ---
-
-        # Cadenza Challenge: CAD2-Task1
-
-        A Causal separation model for the CAD2-Task2 system.
-
-        This model is an ensemble of the following instruments:
-            - Bassoon
-            - Cello
-            - Clarinet
-            - Flute
-            - Oboe
-            - Sax
-            - Viola
-            - Violin
-        Each model is based on the ConvTasNet (Kaituo XU) with multichannel support (Alexandre Defossez). 
-        * Parameters:
-          * B: 256
-          * C: 2
-          * H: 512
-          * L: 20
-          * N: 256
-          * P: 3
-          * R: 3
-          * X: 8
-          * audio_channels: 2
-          * causal: true
-          * mask_nonlinear: relu
-          * norm_type: cLN
-
-        ## Dataset
-        The model was trained using EnsembleSet and CadenzaWoodwind datasets.
-
-        ## How to use
-
-        ```
-        from dynamic_source_separator import DynamicSourceSeparator
-
-        model = DynamicSourceSeparator.from_pretrained(
-            "cadenzachallenge/Dynamic_Source_Separator_Causal"
-        ).cpu()
-
-        ```
+# Cadenza Challenge: CAD2-Task1
+
+A Causal separation model for the CAD2-Task2 system.
+
+This model is an ensemble of the following instruments:
+    - Bassoon
+    - Cello
+    - Clarinet
+    - Flute
+    - Oboe
+    - Sax
+    - Viola
+    - Violin
+Each model is based on the ConvTasNet (Kaituo XU) with multichannel support (Alexandre Defossez). 
+* Parameters:
+  * B: 256
+  * C: 2
+  * H: 512
+  * L: 20
+  * N: 256
+  * P: 3
+  * R: 3
+  * X: 8
+  * audio_channels: 2
+  * causal: true
+  * mask_nonlinear: relu
+  * norm_type: cLN
+
+## Dataset
+The model was trained using EnsembleSet and CadenzaWoodwind datasets.
+
+## How to use
+
+```
+from dynamic_source_separator import DynamicSourceSeparator
+
+model = DynamicSourceSeparator.from_pretrained(
+    "cadenzachallenge/Dynamic_Source_Separator_Causal"
+).cpu()
+
+```